Zobrazeno 1 - 10
of 278
pro vyhledávání: '"FRAZIER, PETER"'
Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite resources, it is desirable to explicitly incorporate function evaluation
Externí odkaz:
http://arxiv.org/abs/2406.20062
Autor:
Tan, Samuel, Frazier, Peter I.
We consider the predict-then-optimize paradigm for decision-making in which a practitioner (1) trains a supervised learning model on historical data of decisions, contexts, and rewards, and then (2) uses the resulting model to make future binary deci
Externí odkaz:
http://arxiv.org/abs/2406.07866
Experimentation with interference poses a significant challenge in contemporary online platforms. Prior research on experimentation with interference has concentrated on the final output of a policy. The cumulative performance, while equally crucial,
Externí odkaz:
http://arxiv.org/abs/2402.01845
Autor:
Buathong, Poompol, Wan, Jiayue, Astudillo, Raul, Daulton, Samuel, Balandat, Maximilian, Frazier, Peter I.
Bayesian optimization is a powerful framework for optimizing functions that are expensive or time-consuming to evaluate. Recent work has considered Bayesian optimization of function networks (BOFN), where the objective function is given by a network
Externí odkaz:
http://arxiv.org/abs/2311.02146
Autor:
Uhrmacher, Adelinde, Frazier, Peter, Hähnle, Reiner, Klügl, Franziska, Lorig, Fabian, Ludäscher, Bertram, Nenzi, Laura, Ruiz-Martin, Cristina, Rumpe, Bernhard, Szabo, Claudia, Wainer, Gabriel A., Wilsdorf, Pia
Simulation has become, in many application areas, a sine-qua-non. Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices and methods. We identify four goals of methodological work for addressi
Externí odkaz:
http://arxiv.org/abs/2310.05649
During the COVID-19 pandemic, safely implementing in-person indoor instruction was a high priority for universities nationwide. To support this effort at the University, we developed a mathematical model for estimating the risk of SARS-CoV-2 transmis
Externí odkaz:
http://arxiv.org/abs/2310.04563
Preferential Bayesian optimization (PBO) is a framework for optimizing a decision maker's latent utility function using preference feedback. This work introduces the expected utility of the best option (qEUBO) as a novel acquisition function for PBO.
Externí odkaz:
http://arxiv.org/abs/2303.15746
In many applications of online decision making, the environment is non-stationary and it is therefore crucial to use bandit algorithms that handle changes. Most existing approaches are designed to protect against non-smooth changes, constrained only
Externí odkaz:
http://arxiv.org/abs/2301.12366
Autor:
Tan, Samuel, Frazier, Peter I.
In practical applications, data is used to make decisions in two steps: estimation and optimization. First, a machine learning model estimates parameters for a structural model relating decisions to outcomes. Second, a decision is chosen to optimize
Externí odkaz:
http://arxiv.org/abs/2210.15576
Ridesharing markets are complex: drivers are strategic, rider demand and driver availability are stochastic, and complex city-scale phenomena like weather induce large scale correlation across space and time. At the same time, past work has focused o
Externí odkaz:
http://arxiv.org/abs/2205.09679